Object detection using color clue and shape feature

Gode, Chetan; Khobragade, Atish

doi:10.1109/wispnet.2016.7566177

Cited by 8 publications

(1 citation statement)

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“…One of the unsupervised learning technique is image clustering. For any particular problem cannot be separated on the basis of a novel multi-dimensional lifting schematic structure of the bandwidth filter bank discussed [11], [12]. The content-based retrieval is worked with types of images, patterns of use, the sensory gap and the role of semantics [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Suport visual details of X-ray image with plain information

Hussein

Abbas²

2021

TELKOMNIKA

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The objective of content-based image retrival (CBIR) is to retrieve relevant medical images from the medical database with reference to the query image in a shorter span of time. All the proposed approaches are different, yet the research goal is to attain better accuracy in a reasonable amount of time. The initial phase of this research presents a feature selection technique that aims to improvise the medical image diagnosis by selecting prominent features. The second phase of the research extracts features and the association rules are formed by the proposed classification based on highly strong association rules (CHiSAR). Finally, the rule subset classifier is employed to classify between the images. The last pert of our work extracts the features from the kidney images and the association rules are reduced for better performance. The image relevance inference is performed and finally, binary and the best first search classification is employed to classify between the images.

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